

import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


a__												=						datasets.logging.get_logger(__name__)


a__												=						'\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n'

a__												=						'\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

a__												=						'\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

a__												=						{
    'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
    'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
    'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
    'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
    'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
    'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
    'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
    'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
    'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
    'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,			_KWARGS_DESCRIPTION  )
class 							UpperCAmelCase_						(     datasets.Metric  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       Tuple:
												return datasets.MetricInfo(
												    description=_DESCRIPTION			,				citation=_CITATION			,				homepage='''https://github.com/google-research/bleurt'''			,				inputs_description=_KWARGS_DESCRIPTION			,				features=datasets.Features(
												        {
												            '''predictions''': datasets.Value('''string'''			,				id='''sequence'''			),
												            '''references''': datasets.Value('''string'''			,				id='''sequence'''			),
												        }			)			,				codebase_urls=['''https://github.com/google-research/bleurt''']			,				reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696''']			,				)


							def 							__lowercase							(     self			,				_a			)       ->       Optional[Any]:
												# check that config name specifies a valid BLEURT model
												if self.config_name == "default":
																	logger.warning(
																	    '''Using default BLEURT-Base checkpoint for sequence maximum length 128. '''
																	    '''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'''			)
																	_a      :       Dict   =						"bleurt-base-128"

												if self.config_name.lower() in CHECKPOINT_URLS:
																	_a      :       Dict   =						self.config_name.lower()

												elif self.config_name.upper() in CHECKPOINT_URLS:
																	_a      :       Any   =						self.config_name.upper()

												else:
																	raise KeyError(
																	    F"""{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}"""			)

												# download the model checkpoint specified by self.config_name and set up the scorer
												_a      :       Union[str, Any]   =						dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]			)
												_a      :       int   =						score.BleurtScorer(os.path.join(_a			,				_a			)			)




							def 							__lowercase							(     self			,				_a			,				_a			)       ->       List[Any]:
												_a      :       List[Any]   =						self.scorer.score(references=_a			,				candidates=_a			)
												return {"scores": scores}





from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:Tuple	,  a						:float		)						->					float:
								return 0.0




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     np.ndarray		,    _lowerCamelCase					:     int)   ->       tuple[int | float, int | float]:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   min([-20, np.min(fft_results[1 : samplerate // 2 - 1])])
							__UpperCamelCase      :  Any       =   max([20, np.max(fft_results[1 : samplerate // 2 - 1])])
							return lowest, highest




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     FilterType		,    _lowerCamelCase					:     int)   ->       None:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   512
							__UpperCamelCase      :  List[Any]       =   [1] + [0] * (size - 1)
							__UpperCamelCase      :  List[Any]       =   [filter_type.process(_lowerCamelCase) for item in inputs]

							__UpperCamelCase      :  Optional[Any]       =   [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__UpperCamelCase      :  Optional[int]       =   np.abs(np.fft.fft(_lowerCamelCase))
							__UpperCamelCase      :  Optional[int]       =   20 * np.logaa(_lowerCamelCase)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24		,    samplerate / 2 - 1)
							plt.xlabel("Frequency (Hz)")
							plt.xscale("log")

							# Display within reasonable bounds
							__UpperCamelCase      :  Optional[Any]       =   get_bounds(_lowerCamelCase		,    _lowerCamelCase)
							plt.ylim(max([-80, bounds[0]])		,    min([80, bounds[1]]))
							plt.ylabel("Gain (dB)")

							plt.plot(_lowerCamelCase)
							plt.show()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     FilterType		,    _lowerCamelCase					:     int)   ->       None:






							'''simple docstring'''





							__UpperCamelCase      :  Any       =   512
							__UpperCamelCase      :  Dict       =   [1] + [0] * (size - 1)
							__UpperCamelCase      :  Tuple       =   [filter_type.process(_lowerCamelCase) for item in inputs]

							__UpperCamelCase      :  Dict       =   [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__UpperCamelCase      :  Optional[int]       =   np.angle(np.fft.fft(_lowerCamelCase))

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24		,    samplerate / 2 - 1)
							plt.xlabel("Frequency (Hz)")
							plt.xscale("log")

							plt.ylim(-2 * pi		,    2 * pi)
							plt.ylabel("Phase shift (Radians)")
							plt.plot(np.unwrap(_lowerCamelCase		,    -2 * pi))
							plt.show()